In this section, we show efficacy of our optimization framework when applied to the task of image classification on CIFAR100 [1]}
dataset. CIFAR100 dataset contains 100 classes, 50,000 images in the train set and 10,000 images in the test set.
Therefore, in our framework, along with the model parameters, we learn 100 and 50,000 dynamic learning
rates for class and instances respectively.
We evaluate our framework with ResNet18 [2]}, VGG16 [3]}.
We use standard setup for training both architectures (details in supplementary).
